Systems and method for intent messaging

ABSTRACT

Disclosed embodiments provide a framework to assist customers in obtaining relevant responses from brands and other users to the intents communicated by these customers. In response to obtaining an intent, an intent messaging service identifies one or more users that can be provided with the intent to solicit responses to the intent. The one or more users are selected based on characteristics of the intent. The intent messaging service evaluates the responses to the intent from the one or more users to identify relevant responses that can be presented to the customer. The intent messaging service provides the relevant responses to the intent to the customer, which can determine which users to interact with to address the intent.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present patent application claims the priority benefit of U.S.provisional patent application No. 63/033,529 filed Jun. 2, 2020, thedisclosures of which are incorporated by reference herein.

FIELD

The present disclosure relates generally to systems and methods forfacilitating messaging between customers and brands. More specifically,techniques are provided to deploy a framework to assist customers inobtaining relevant responses from brands and other users to the intentscommunicated by these customers.

SUMMARY

Disclosed embodiments provide a framework for an intent processingsystem that allows customers to interact with brands and other users toobtain relevant responses to their intents. According to someembodiments, a computer-implemented method is provided. Thecomputer-implemented method comprises obtaining an intent. The intentcorresponds to a request that is to be addressed. Further, the intent isassociated with a customer. The computer-implemented method furthercomprises identifying one or more users to which to provide the intent.The one or more users are identified based on a set of characteristicsof the intent. The computer-implemented method further comprisesproviding the intent. The intent is provided to the one or more users tosolicit responses to the intent. The computer-implemented method furthercomprises obtaining a set of responses to the intent. Thecomputer-implemented method further comprises evaluating the set ofresponses to identify relevant responses to the intent. Irrelevantresponses from the set of responses are discarded. Thecomputer-implemented method further comprises providing the relevantresponses to cause the relevant responses to be presented to thecustomer in response to the intent.

In an example, a system comprises one or more processors and memoryincluding instructions that, as a result of being executed by the one ormore processors, cause the system to perform the processes describedherein. In another example, a non-transitory computer-readable storagemedium stores thereon executable instructions that, as a result of beingexecuted by one or more processors of a computer system, cause thecomputer system to perform the processes described herein.

This summary is not intended to identify key or essential features ofthe claimed subject matter, nor is it intended to be used in isolationto determine the scope of the claimed subject matter. The subject mattershould be understood by reference to appropriate portions of the entirespecification of this patent application, any or all drawings, and eachclaim.

The foregoing, together with other features and examples, will bedescribed in more detail below in the following specification, claims,and accompanying drawings.

Various embodiments of the disclosure are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationscan be used without parting from the spirit and scope of the disclosure.Thus, the following description and drawings are illustrative and arenot to be construed as limiting. Numerous specific details are describedto provide a thorough understanding of the disclosure. However, incertain instances, well-known or conventional details are not describedin order to avoid obscuring the description. References to one or anembodiment in the present disclosure can be references to the sameembodiment or any embodiment; and, such references mean at least one ofthe embodiments.

Reference to “one embodiment” or “an embodiment” means that a particularfeature, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of the phrase “in one embodiment” in various places in thespecification are not necessarily all referring to the same embodiment,nor are separate or alternative embodiments mutually exclusive of otherembodiments. Moreover, various features are described which can beexhibited by some embodiments and not by others.

The terms used in this specification generally have their ordinarymeanings in the art, within the context of the disclosure, and in thespecific context where each term is used. Alternative language andsynonyms can be used for any one or more of the terms discussed herein,and no special significance should be placed upon whether or not a termis elaborated or discussed herein. In some cases, synonyms for certainterms are provided. A recital of one or more synonyms does not excludethe use of other synonyms. The use of examples anywhere in thisspecification including examples of any terms discussed herein isillustrative only, and is not intended to further limit the scope andmeaning of the disclosure or of any example term. Likewise, thedisclosure is not limited to various embodiments given in thisspecification.

Without intent to limit the scope of the disclosure, examples ofinstruments, apparatus, methods and their related results according tothe embodiments of the present disclosure are given below. Note thattitles or subtitles can be used in the examples for convenience of areader, which in no way should limit the scope of the disclosure. Unlessotherwise defined, technical and scientific terms used herein have themeaning as commonly understood by one of ordinary skill in the art towhich this disclosure pertains. In the case of conflict, the presentdocument, including definitions will control.

Additional features and advantages of the disclosure will be set forthin the description which follows, and in part will be obvious from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is described in conjunction with the appendedFigures:

FIG. 1 shows an illustrative example of an environment in which anintent messaging service obtains an intent from a customer and presentsthe customer with responses to the intent from brands and other users inaccordance with at least one embodiment;

FIG. 2 shows an illustrative example of an environment in which anintent messaging service identifies brands and other users to solicitresponses to an intent submitted by a customer in accordance with atleast one embodiment;

FIG. 3 shows an illustrative example of an environment in which acustomer, via a user interface, generates an intent to solicit aresponse from brands and other users of an intent messaging service inaccordance with at least one embodiment;

FIG. 4 shows an illustrative example of an environment in which acustomer is provided with a status with regard to an intent submitted bythe customer via a user interface in accordance with at least oneembodiment;

FIG. 5 shows an illustrative example of an environment in which acustomer, via a user interface, introduces another user or brand to anexisting conversation based on a response to an intent of the customerin accordance with at least one embodiment;

FIG. 6 shows an illustrative example of an environment in which acustomer, via a user interface, initiates a communications channel witha brand to converse with the brand with regard to an intent inaccordance with at least one embodiment;

FIG. 7 shows an illustrative example of an environment in which acustomer, via a user interface, provides a response to an intent ofanother customer in accordance with at least one embodiment;

FIG. 8 shows an illustrative example of a process for obtaining anintent and providing the intent to other users and brands in accordancewith at least one embodiment;

FIG. 9 shows an illustrative example of a process for evaluatingproposed responses from brands and other users to identify relevantresponses presentable to a customer in response to an intent inaccordance with at least one embodiment; and

FIG. 10 shows an illustrative example of an environment in which variousembodiments can be implemented.

In the appended figures, similar components and/or features can have thesame reference label. Further, various components of the same type canbe distinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If only the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label.

DETAILED DESCRIPTION

The ensuing description provides preferred examples of embodiment(s)only and is not intended to limit the scope, applicability orconfiguration of the disclosure. Rather, the ensuing description of thepreferred examples of embodiment(s) will provide those skilled in theart with an enabling description for implementing a preferred examplesof embodiment. It is understood that various changes can be made in thefunction and arrangement of elements without departing from the spiritand scope as set forth in the appended claims.

FIG. 1 shows an illustrative example of an environment 100 in which anintent messaging service 102 obtains an intent from a customer 108 andpresents the customer with responses to the intent from brands 112 andother users 114 in accordance with at least one embodiment. An intentmay correspond to an issue that a customer wishes to have resolved.Examples of intents can include (for example) topic, sentiment,complexity, and urgency. A topic can include, but is not limited to, asubject, a product, a service, a technical issue, a use question, acomplaint, a refund request or a purchase request, etc. An intent can bedetermined, for example, based on a semantic analysis of a message(e.g., by identifying keywords, sentence structures, repeated words,punctuation characters and/or non-article words); user input (e.g.,having selected one or more categories); and/or message-associatedstatistics (e.g., typing speed and/or response latency).

In the environment 100, a customer 108, via an intent messagingapplication implemented on a computing device 110, transmits a requestto an intent processing system 104 of an intent messaging service 102 toobtain one or more responses to the request from one or more brands 112and/or other users 114 associated with the intent messaging service 102.The other users 114 may include members of the intent messaging service102 community that may also interact with brands 112 to obtain responsesto their intents and that may have experience with regard to varioustopics, goods, services, or other areas that may be associated withintents. The intent messaging application implemented on the computingdevice 110 may be provided by the intent messaging service 102 to allowcustomers, such as customer 108, to interact with brands 112 and otherusers 114 that are associated with the intent messaging service 102(e.g., community of users that utilize the intent messaging service 102)and may provide goods and services to customers. The intent messagingservice 102 may provide a platform for customers and brands to connectin order to provide recommendations and advice with regard to intentssubmitted by these customers.

In response to obtaining a request from a customer 108, the intentprocessing system 104 may evaluate the request to extract an intentexpressed by the customer 108 and that may be used to identify thebrands 112 and other users 114 that may provide relevant responses tothe customer's request. In an embodiment, the intent processing system104 utilizes a machine learning model to process the request in order toidentify and extract the intent from the request. The machine learningmodel may be used to perform a semantic analysis of the request (e.g.,by identifying keywords, sentence structures, repeated words,punctuation characters and/or non-article words) to identify the intentexpressed in the request. The machine learning model utilized by theintent processing system 104 may be dynamically trained using supervisedlearning techniques. For instance, a dataset of input requests and knownintents included in the input requests can be selected for training ofthe machine learning model. In some implementations, known intents usedto train the machine learning model may include characteristics of theseintents. The machine learning model may be evaluated to determine, basedon the input sample requests supplied to the machine learning model,whether the machine learning model is extracting the expected intentsfrom each of the requests. Based on this evaluation, the machinelearning model may be modified to increase the likelihood of the machinelearning model generating the desired results. The machine learningmodel may further be dynamically trained by soliciting feedback fromcustomers, including customer 108, with regard to the extracted intentobtained from submitted requests. For instance, prior to submitting anextracted intent for identification of one or more brands 112 or otherusers 114 from which to solicit responses to the intent, the extractedintent may be presented to the customer 108 to determine whether theextracted intent corresponds to the request submitted by the customer108. The response from the customer 108 may, thus, be utilized to trainthe machine learning model based on the accuracy of the machine learningmodel in identifying the intent from the request.

The intent processing system 104 may provide the extracted intent to anintent matching system 106 of the intent messaging service 102. Theintent matching system 106 may be implemented on a computing system orother system (e.g., server, virtual machine instance, etc.) of theintent messaging service 102. Alternatively the intent matching system106 may be implemented as an application or other process executed on acomputing system of the intent messaging service 102. In an embodiment,in response to obtaining a new intent from the intent processing system104, the intent matching system 106 utilizes the new intent, as well asinformation regarding different brands 112 and other users associatedwith the intent messaging service 102, as input to a machine learningmodel to identify one or more brands 112 and/or other users 114 that maybe likely to provide relevant responses to the intent. The informationregarding the different brands 112 and other users 114 may includehistorical data corresponding to responses submitted by the differentbrands 112 and other users 114 to previously provided intents. Thehistorical data may indicate a brand's or other user's performance inproviding relevant responses to different intents submitted to the brandor other user, information regarding any prior interactions between thebrand/other user and the customer 108, customer feedback (if any)related to a customer's interaction with the brand or other user, andthe like. Further, the information regarding a brand or other user mayspecify the types of intents that the brand or other user may haveexpertise in responding to. For instance, the information may indicatewhat goods and services are provided by a brand, what goods or servicesa user has utilized, or any other information that may be useful inidentifying whether resolution of a particular intent may involve thesegoods and services.

The machine learning model utilized by the intent matching system 106may be dynamically trained using sample intents and sample outputscorresponding to features of brands 112 and other users 114 that may beused to identify the brands 112 and other users 114 to which an intentis to be provided. Further, the machine learning model may bedynamically trained using feedback from the different brands 112 andother users 114 receiving an intent. This feedback may be used todetermine whether the machine learning model is selecting brands 112 andother users 114 that are capable of responding to an intent with arelevant response or that are otherwise associated with a feature of anintent. This feedback may be used to further train the machine learningmodel utilized by the intent matching system 106. Thus, the machinelearning model may be dynamically trained in real time as feedback isobtained by the intent matching system 106 from different brands 112 andother users 114 for myriad intents submitted to these different brands112 and other users 114. Alternatively, the machine learning model maybe trained periodically based on obtained feedback from the brands 112and other users 114.

The output of the machine learning model utilized by the intent matchingsystem 106 may include identifiers corresponding to the one or morebrands 112 and/or other users 114 to which the new intent is to beprovided in order to solicit responses from each of the one or morebrands 112 and/or other users 114. The new intent may be assigned aunique identifier such that responses obtained from the one or morebrands 112 and/or other users 114 may be associated with the particularintent by reference to the unique identifier. In an embodiment, theintent matching system 106 updates the user interface of the intentmessaging application utilized by each brand 112 or other user 114 toindicate solicitation of a response to a particular intent. Forinstance, through this user interface, the intent matching system 106may present an agent associated with the brand 112 or other user 114with the new intent, as well as options for responding to the intent. Anagent associated with the brand 112 or other user 114, via this userinterface, may respond to the intent, reject/ignore the intent, orprovide an indication to the intent matching system 106 that the intentis not relevant to the brand 112 or other user. The action performed bythe agent associated with the brand 112 or other user 114 may be used tofurther dynamically train the machine learning model utilized by theintent matching system 106 to select brands 112 and other users forresponding to intents. For instance, if an agent associated with a brandindicates that a provided intent is not relevant to the brand, theintent matching system may utilize this feedback to train the machinelearning model such that irrelevant intents are not provided to thebrand.

In an embodiment, a brand 112 (e.g., an agent associated with the brandthat is assigned to interact with the intent messaging service 102) orother user 114 receiving an intent is restricted to a single response tothe intent. For instance, when an agent associated with a brand 112 orother user 114 submits a response to an intent to the intent processingsystem 104, the intent messaging application utilized by the agentassociated with the brand 112 or other user 114 may disable the abilityto submit additional responses to the intent until the customer 108 hasindicated that it wishes to engage further with the brand 112 or otheruser 114 with regard to the intent. This may prevent a brand 112 orother user 114 from inundating (e.g., “spamming”) the customer 108 withresponses to the intent. Further, should a customer 108 refuse to engagewith the brand 112 or other user 114, the customer 108 may be sparedfrom additional responses from the brand 112 or other user 114.

If an agent associated with a brand 112 or other user 114 submits anintent response to the intent processing system 104 for the customer108, the intent processing system 104 may evaluate the intent responseto determine whether the intent response is relevant to the intentsubmitted by the customer 108. In an embodiment, the intent processingsystem 104 utilizes a classification algorithm or other machine learningmodel to classify an intent response as either being relevant to theintent or irrelevant to the intent. The classification algorithm orother machine learning model utilized by the intent processing system104 may be dynamically trained using supervised learning techniques. Forinstance, a dataset of input intents, known relevant responses, knownirrelevant responses, and classifications, can be selected for trainingof the classification algorithm or other machine learning model. In someexamples, the input intents can be obtained from administrators of theintent messaging service, customers of the intent messaging service, orother sources associated with the intent messaging service. In someimplementations, known relevant and irrelevant responses used to trainthe classification algorithm or other machine learning model utilized bythe intent processing system include responses generated by the entitiesthat generated the sample intents. Further, the classification algorithmor other machine learning model used to classify intent responses asbeing relevant or irrelevant may be trained using feedback fromcustomers, including customer 108. For instance, if a responseclassified as being relevant to an intent is provided to a customer 108,the customer 108 may provide feedback indicating whether the responsewas indeed relevant to the intent. This feedback may be used to eitherreinforce the model (e.g., a response classified as being relevant wasdeemed relevant to the intent by the customer) or to update the model(e.g., a response classified as being relevant was deemed to beirrelevant to the intent by the customer). As customers of the intentmessaging service 102 provide this feedback to the intent messagingservice 102, the classification algorithm or other machine learningmodel may be dynamically updated in real time.

The intent processing system 104, using the classification algorithm ormachine learning model described above, may discard any responsesclassified as being irrelevant to the intent. In an embodiment, if theintent processing system 104 determines that a brand 112 or other userhas provided an irrelevant response to an intent, the intent processingsystem 104 updates the profile of the brand 112 or other user toindicate that it has provided an irrelevant response to the intent. Thismay reduce the likelihood of the brand 112 or other user 114 from beingselected by the intent matching system 106 to respond to similarintents. In some instances, this feedback may also be used todynamically train the machine learning model utilized by the intentmatching system 106 to further reduce the likelihood of the brand 112 orother user 114 being solicited to provide responses to similar intents.If a brand 112 or other user 114 is deemed to be providing irrelevantresponses on a consistent basis, other remedial operations may beperformed, such as disassociating or removing the brand 112 or otheruser from the intent messaging service 102.

If the intent processing system 104 determines that a relevant responseto an intent has been obtained, the intent processing system 104 mayprovide this response to the customer 108 via the intent messagingapplication operating on the computing device 110. This may cause theintent response to be displayed on a user interface of the computingdevice 110. Through this user interface, the customer 108 may evaluatethe intent response and determine whether to engage further with thebrand 112 or other user 114, thank or express gratitude to the brand 112or other user for the response, or ignore the intent response.Additionally, the customer 108 may provide feedback to the intentprocessing system 104 with regard to the quality of the intent response.This feedback may be utilized to further dynamically train the machinelearning model utilized by the intent matching system 106 used toidentify brands 112 and other users 114 to which intents are provided tosolicit responses to the intents.

In an embodiment, if the customer 108 determines that it wants tocommunicate with a brand 112 or other user 114 further with regard tothe intent submitted by the customer 108, the intent messaging service102 establishes a communications channel between the customer 108 andthe brand 112 or other user 114 through which the customer 108 and brand112 or other user 114 may exchange messages and other content. As notedabove, when a brand 112 or other user 114 submits a response to anintent, the intent messaging application utilized by the brand 112 orother user may prohibit the brand 112 or other user 114 from submittingadditional responses. However, if the customer 108 indicates that itwishes to converse with the brand 112 or other user 114 with regard tothe intent, the intent messaging service 102 may transmit an instructionor other indication to the intent messaging application to enable thebrand 112 or other user 114 to submit additional responses to thecustomer 108 vis the intent messaging application. Further, the intentmessaging service 102 may provide the brand 112 or other user withadditional information regarding the customer 108 (e.g., customer name,customer address, customer images, customer contact information, etc.).

FIG. 2 shows an illustrative example of an environment 200 in which anintent messaging service 202 identifies brands 220 and other users 222to solicit responses to an intent submitted by a customer 218 inaccordance with at least one embodiment. In the environment 200, theintent messaging service 202 obtains a request from a customer 218 tosolicit responses from one or more brands 220 and/or other users 222with regard to a particular intent. For instance, the customer 218,using an intent messaging application installed on a computing deviceutilized by the customer 218 or otherwise accessed by the customer 218using the computing device (e.g., via a website, etc.), may generate therequest and indicate, in the request, the intent for which the customer218 is seeking relevant responses. The intent may (for example) be atopic, sentiment, complexity, and/or level of urgency. A topic caninclude, but is not limited to, a subject, a product, a service, atechnical issue, a use question, a complaint, a refund request or apurchase request, etc.

The customer 218, via the intent messaging application, may transmit itsrequest and corresponding intent to a customer messaging system 206 ofan intent processing system 204. The customer messaging system 206 maybe implemented on a computing system of the intent messaging service 202or as an application executed by a computing system of the intentprocessing system 204. The customer messaging system 206 may facilitatecommunications between the customer 218 and the intent messaging service202 and any brands 220 and/or other users 222 associated with the intentmessaging service 202. For instance, the customer messaging system 206may establish a communications channel with any brand 220 or other user222 that the customer 218 has selected to engage in a conversation viathe intent messaging application concerning a particular intent.Further, the customer messaging system 206 may serve to update the userinterface of the intent messaging application based on the providedintent and responses obtained from the intent messaging service 202identifying brands 220 and other users 222 from which intent responsesmay be solicited for the particular intent.

In response to obtaining the request and corresponding intent from thecustomer 218, the customer messaging system 206 may transmit the requestand corresponding intent to the intent extraction engine 208 of theintent processing system 204 to extract the intent from the request. Theintent extraction engine 208 may be implemented as a computer systemthat utilizes a machine learning model to process an incoming requestfrom a customer 218 to identify the intent expressed in the request. Forinstance, the machine learning model may be used to perform a semanticanalysis of the request (e.g., by identifying keywords, sentencestructures, repeated words, punctuation characters and/or non-articlewords) to identify the intent expressed in the request. An intent canalso be determined, for example, based on user input (e.g., havingselected one or more categories); and/or message-associated statistics(e.g., typing speed and/or response latency).

The machine learning model utilized by the intent extraction engine 208may be trained using supervised learning techniques. For instance, adataset of input requests and known intents included in the inputrequests can be selected for training of the machine learning modelimplemented by the intent extraction engine 208. In some examples, theinput requests can be obtained from administrators of the intentmessaging service 202, customers of the intent messaging service 202, orother sources associated with the intent messaging service 202. In someimplementations, known intents used to train the machine learning modelutilized by the intent extraction engine 208 may include characteristicsof these intents provided by the entities that generated the samplerequests. The machine learning model may be evaluated to determine,based on the input sample requests supplied to the machine learningmodel, whether the machine learning model is extracting the expectedintents from each of the requests. Based on this evaluation, the machinelearning model may be modified (e.g., one or more parameters orvariables may be updated) to increase the likelihood of the machinelearning model generating the desired results (e.g., expected intents).

In an embodiment, the intent extraction engine 208 evaluates the requestfrom the customer 208 to determine whether additional information isrequired in order to extract or otherwise supplement the intent from therequest in order to allow for identification of brands 220 and/or otherusers 222 that may provide relevant responses to the intent. Forinstance, the intent extraction engine 208 may determine that thecustomer's geographic location and timeframe for resolution of theintent are required. The intent extraction engine 208 may transmit arequest to the customer messaging system 206 to solicit this additionalinformation from the customer 218. In an embodiment, the customermessaging system 206 utilizes natural language processing (NLP) or otherartificial intelligence to solicit the customer 208 for the requestedinformation. For example, using NLP or other artificial intelligence,the customer messaging system 206 may ask the customer 218 to provideits location, the timeframe for resolution of the intent, which users orbrands the customer 218 would like to solicit responses from, and thelike. Responses provided by the customer 218 may be provided to theintent extraction engine 208, which may use these responses and thesupplied request from the customer 218 to extract the intent andsupplement the intent with the additional information provided by thecustomer 218. This may be used by the intent matching system 210 toidentify the brands 220 and/or other users 222 that may be solicited toobtain responses to the intent.

The intent extraction engine 208 may provide an extracted intent fromthe request submitted by the customer 218 to an intent machine learningmodeling engine 212 of the intent matching system 210. The intentmachine learning modeling engine 212 may be implemented on a computingsystem of the intent matching system 210 or otherwise as an applicationor process executed on a computing system of the intent matching system210. In an embodiment, the intent machine learning modeling engine 212implements a machine learning model configured to identify brands 220and/or other users 222 to which an intent may be supplied in order tosolicit responses to the intent. The machine learning model implementedby the intent machine learning modeling engine 212 may utilize a branddatabase 214 and user database 216 maintained by the intent matchingsystem 210, as well as the customer's intent, as input to a machinelearning model to identify the brands 220 and/or users 222 that arelikely to provide relevant responses to the intent. The brand database214 and user database 216 may include profiles of each of the brands 220and users 222, respectively, which may be associated with the intentmessaging service 202. Each profile may indicate a user's or brand'sexperience responding to particular intents or categories of intents, aswell as the user's or brand's interest in the underlying topic orclassification of the intent. Further, each profile may indicatefeedback with regard to a user's or brand's response to previouslyprovided intents. This feedback may specify whether a response providedfor a particular intent was relevant, useful, or otherwise appreciatedby the corresponding customer.

The user database 216 may further include a profile of the customer 218.The profile of the customer 218 may indicate any customer preferencesfor particular brands 220 or other users 222. For instance, the profileof the customer 218 may specify which brands 220 or other users 222 thecustomer 218 has interacted with to address previously supplied intents.Further, for each of these interactions, the profile may includefeedback from the customer 218. This feedback may indicate whetherinteraction with a particular brand or other user was relevant to thecorresponding intent, useful in addressing the corresponding intent, orotherwise conducive to a positive experience with the brand or otheruser. This information may also be utilized by the intent machinelearning modeling engine 212 to identify which brands 220 and/or otherusers 222 to solicit in order to obtain a response to the intent.

The machine learning model utilized by the intent machine learningmodeling engine 212 may be trained using sample intents and sampleoutputs corresponding to features of brands 220 and other users 222 thatmay be used to identify the brands 220 and other users 222 to which anintent is to be provided. Further, the machine learning model may betrained using feedback from the different brands 220 and other users 222receiving an intent. This feedback may be used to determine whether themachine learning model is selecting brands 220 and other users 222 thatare capable of responding to an intent with a relevant response or thatare otherwise associated with a feature of an intent. For example, if abrand that provides interior design services obtained an intent that isnot related to interior design, the brand may provide feedbackindicating that the provided intent is not relevant to the brand. Thisfeedback may be used to further train the machine learning modelutilized by the intent machine learning modeling engine 212.

The output produced by the machine learning model implemented by theintent machine learning modeling engine 212 may include identifierscorresponding to the brands 220 and/or other users 222 to which theintent is to be provided in order to solicit responses from the brands220 and/or other users 222. Based on this output, the intent machinelearning modeling engine 212 may transmit the intent to the identifiedbrands 220 and/or other users 222. For instance, the intent machinelearning modeling engine 212 may update a user interface of an intentmessaging application of each of the identified brands 220 and/or otherusers 222 to present the intent. The intent may be assigned a uniqueidentifier such that responses from the brands 220 and/or other users222 to the intent may be associated with the intent by the uniqueidentifier. A brand or other user receiving the intent via the intentmessaging application may respond to the intent and submit the responseto the intent processing system 204, which may evaluate the response todetermine whether the response is relevant to the intent.

In an embodiment, the intent is provided anonymously to the identifiedbrands 220 and/or other users 222. For instance, the intent machinelearning modeling engine 212 may remove any identifying information ofthe customer 218 (e.g., customer name, customer address, customercontact information, etc.) when supplying the intent to the identifiedbrands 220 and/or other users 222. However, the intent machine learningmodeling engine 212 may provide, in addition to the intent, otherinformation that may be useful to the brand or other user in preparing aresponse to the intent. For instance, the intent may be provided with ageneral location of the customer 218 (e.g., city, state, etc.). Thisgeneral location can be used to determine whether the brand or otheruser can provide a response to the intent that may be indicative of anability to assist the customer 218 at the general location (e.g., brandmay maintain a shop within the general location, a user provides goodsand services at the general location, etc.).

In an embodiment, once a brand or other user submits a response to theintent, the intent messaging service 202 may disable the brand's orother user's ability to provide additional responses to the intent viathe intent messaging application. This prevents brands or other usersfrom potentially inundating the customer 218 with responses to aparticular intent. A conversation with the customer 218, via the intentmessaging application, may be established upon a request from thecustomer 218 to initiate a communications channel with the particularbrand 220 or other user 222.

In an embodiment, the intent processing system 204, in response toobtaining an intent response from a brand or other user, utilizes aclassification algorithm or other machine learning model to evaluate theintent response in order to classify the intent response as either beingrelevant to the intent or irrelevant to the intent. Any responsesclassified as being irrelevant to the intent may be discarded by theintent processing system 204. Responses that are classified as beingrelevant to the intent may be presented to the customer 218 by thecustomer messaging system 206 via the intent messaging applicationimplemented on the customer's computing device. Further, the intentresponse may be provided with options for the customer 218 to invite thebrand or other user that submitted the intent response to a conversationwith regard to the intent, to thank the brand or other user for theirintent response, to ignore the intent response from the brand or otheruser, to ignore future responses from the brand or other user (e.g.,block the brand or other user), and the like. If the customer 218 optsto converse with the brand or other user based on the provided intentresponse, the customer messaging system 206 may establish acommunications channel between the customer 218 and the brand or otheruser to allow for the customer 218 and the brand or other user toconverse using their respect intent messaging applications. In anembodiment, the customer messaging system 206 provides, to the brand orother user, customer information (e.g., name, images, address, contactinformation, etc.) upon establishing the communications channel.Further, the customer messaging system 206 re-enables the brand or otheruser to transmit messages or responses to the customer 218 over thenewly established communications channel.

Responses to an intent from the brands 220 and/or other users 222 may beevaluated by the intent processing system 204 to determine theirrelevance to the intent of the customer 218.

FIG. 3 shows an illustrative example of an environment 300 in which acustomer 306, via a user interface 310, generates an intent to solicit aresponse from brands 304 and other users 305 of an intent messagingservice 302 in accordance with at least one embodiment. In theenvironment 300, a customer 306, via an intent messaging applicationoperating on a computing device 308, generates a new intent that can besubmitted to an intent messaging service 302 to solicit one or moreresponses from brands 304 and other users 305 associated with the intentmessaging service 302. For instance, using the intent messagingapplication via the user interface 310, the customer 306 may define arequest or intent for which the customer 306 is seeking responses frombrands 304 and other users 305 associated with the intent messagingapplication 302.

As illustrated in FIG. 3, the customer 306, via the user interface 310,may be presented with an option to provide a title for its request orintent, as well as an option to define the request or intent. Forexample, the customer 306 may provide, as a title to its request orintent, “Interior Design Recs” for a request or intent that serves as asolicitation from the customer 306 for recommendations for interiordesigners that may be of service to the customer 306. Further, via theuser interface 310, the customer 306 may define the parameters of therequest or intent (e.g., “I'm looking for a good interior designer”).This information may be utilized by the intent messaging service 302 toextract the intent of the customer 306 and identify one or more brands304 and other users 305 from which to solicit responses to the intentsubmitted by the customer 306.

In an embodiment, the intent messaging service 302 can utilize naturallanguage processing or other artificial intelligence to query thecustomer 306 for additional information that can be used to supplementthe intent and allow for a tailored identification of brands 304 andother users 305 that may be likely to provide relevant responses to theintent. For instance, the intent messaging service 302 may query thecustomer 306 to identify a location of the customer, whether other userscan help the customer with its intent, what the timeframe is forresolution of the intent, and the like. Responses provided by thecustomer 306 to the intent messaging service 302 may be used to furthernarrow selection of brands 304 and other users 305 from which responsesto the intent may be solicited.

In an embodiment, the customer 306 can be presented, via the userinterface 310 by the intent messaging application, with a listing ofcontacts or other participants that may be added to the conversationwith regard to the submitted intent. For instance, the intent messagingapplication may obtain a listing of contacts from the customer'scomputing device 308, which may be presented via the user interface 310.Alternatively, the intent messaging application may obtain, from theintent messaging service 302, a listing of contacts associated with thecustomer 306 that also utilize the intent messaging service 302. Thislisting of contacts from the intent messaging service 302 may includeusers that the customer 306 has designated as “friends” or otherwiseable to directly communicate with the customer 306 via the intentmessaging service 302.

In an embodiment, via the user interface 310, the intent messagingservice 302 can further provide recommendations for brands 304 and/orother users 305 from which the customer 306 can solicit a response tothe submitted intent. For instance, based on an evaluation of the intentby the intent messaging service 302, the intent messaging service 302may recommend one or more brands 304 or other users 305 that may belikely to provide relevant responses to the submitted intent. The intentmessaging service 302 may present these one or more brands 304 or otherusers 305 to allow the customer 306 to select which brands 304 or otherusers 305 are to receive the intent in order to solicit a response tothe intent. Based on the customer's selection, the intent messagingservice 302 may solicit a response to the intent from the selectedbrands and/or other users.

FIG. 4 shows an illustrative example of an environment 400 in which acustomer 406 is provided with a status with regard to an intentsubmitted by the customer 406 via a user interface 410 in accordancewith at least one embodiment. In the environment 400, in response tosubmitting a new intent to the intent messaging service 402 to solicitresponses to the intent from one or more brands 404 and other users 405,the intent messaging service 402 may evaluate the intent and profiles ofdifferent brands and users associated with the intent messaging service402 to identify a subset of brands 404 and other users 405 that may besolicited to provide a response to the intent. In an embodiment, theintent messaging service 402, using a machine learning model implementedby an intent matching system, identifies brands 404 and other users 405that are likely to provide relevant responses to the intent submitted bythe customer 406. The machine learning model may utilize a branddatabase and user database maintained by the intent matching system, aswell as the customer's intent, as input to a machine learning model toidentify the brands and/or users that are likely to provide relevantresponses to the intent. The brand database and user database mayinclude profiles of each of the brands and users, respectively, whichmay be associated with the intent messaging service 402. Each profilemay indicate a user's or brand's experience responding to particularintents or categories of intents, as well as the user's or brand'sinterest in the underlying topic or classification of the intent.Further, each profile may indicate feedback with regard to a user's orbrand's response to previously provided intents. This feedback mayspecify whether a response provided in response to an intent wasrelevant, useful, or otherwise appreciated by the correspondingcustomer.

In response to obtaining an intent from the customer 406, the intentmessaging service 402 may update the user interface 410 to provide a newconversation window for the provided intent. The name or title of theconversation window presented via the user interface 410 may correspondto the name or title of the request submitted by the customer 406 to theintent messaging service 402 via the user interface 410. Further, viathe user interface 410 and within the conversation window for theintent, the intent messaging service 402 may provide a status withregard to the processing of the customer's provided intent. Forinstance, as illustrated in FIG. 4, the intent messaging service 402 mayindicate that the intent has been shared with the intent messagingservice's community (e.g., brands 404 and other users 405 solicited toprovide a response to the intent). Further, the intent messaging service402 may indicate, via the user interface 410, that any vetted responsesfrom the brands 404 and/or other users 405 may be presented to thecustomer 406 within the conversation window related to the intentsubmitted by the customer 406.

In an embodiment, the customer 406 can introduce one or more othercontacts to the conversation window in order to enable communicationbetween the customer 406 and these one or more contacts within theconversation window. For instance, the customer 406 may select, from alisting of available contacts, one or more contacts that the customer406 may invite to partake in the conversation tied to the submittedintent. This listing of available contacts may be maintained by theintent messaging service 402 and may be provided to the customer 406 viathe intent messaging application operating on computing device 408.Alternatively, the listing of available contacts may be maintained onthe computing device 408. If the customer 406 selects one or morecontacts from this listing, the intent messaging service 402 may obtaincontact information for each of these one or more contacts and transmita notification to each of these one or more contacts to invite these oneor more contacts to partake in the conversation with the customer 406with regard to the intent. If the listing of contacts is maintained onthe computing device 408, the intent messaging application may obtainthe contact information from the computing device 408 and provide thiscontact information to the intent messaging service 402 to provide anotification to these contacts with regard to the conversation. In anembodiment, the notification can include a Uniform Resource Identifier(URI) or other network address of the conversation. A contact may accessthe conversation by utilizing the URI or other network address providedby the intent messaging service 402.

As responses to the intent are obtained from the brands 404 and otherusers 405 to which the intent was provided to solicit a response, theintent messaging service 402 may determine these responses to determinewhether the responses are relevant and responsive to the intentsubmitted by the customer 406. In an embodiment, the intent messagingservice 402 utilizes a classification algorithm or other machinelearning model to classify responses to an intent as being eitherrelevant to the intent or irrelevant to the intent. Responses classifiedas being irrelevant to the intent are discarded by the intent messagingservice 402 and may not be presented to the customer 406 via the userinterface 410. However, any responses classified as being relevant tothe intent submitted by the customer 406 may be presented to thecustomer 406 via the user interface 410. For instance, the intentmessaging service 402 may transmit the relevant (e.g., vetted) responsesto the intent messaging application on the customer's computing device408 to cause the intent messaging application to update the conversationwindow associated with the intent to present the obtained responses. Thecustomer 406 may select any of the obtained responses to the intent toinitiate a conversation with the brand 404 or other user 405 thatsupplied the selected response. This conversation may be presentedwithin the conversation window, through which the customer 406 maycommunicate with the brand 404 or other user 405.

FIG. 5 shows an illustrative example of an environment 500 in which acustomer 506, via a user interface 510, introduces another user 504 orbrand to an existing conversation based on a response to an intent ofthe customer 506 in accordance with at least one embodiment. In theenvironment 500, a customer 506 is presented, via a user interface 510of an intent messaging application operating on a computing device 508of the customer 506, with a vetted response from another user 504 to anintent submitted by the customer 506 to the intent messaging service502. As noted above, an intent may be provided to select brands andother users based on one or more factors. For instance, another user 504may be presented with an intent of the customer 506 as a result of theother user 504 having experience responding to similar intents and/orsubmitting similar intents to the intent messaging service 502. Further,the other user 504 may be presented with the intent as a result of theother user's interest in the topic of the intent submitted to the intentmessaging service 502. In an embodiment, the other user 504 is presentedwith the intent as a result of a determination generated using a machinelearning model that may utilize the intent, as well as brand and userprofiles from respective databases, as input to generate an output thatincludes identifiers of the selected brands and users to which theintent is to be provided.

Responses to an intent from brands and other users 504 may be evaluatedby the intent messaging service 502 to determine whether these responsesare relevant to a particular intent. For instance, the intent messagingservice 502 may utilize a classification algorithm or other machinelearning model to evaluate responses to an intent to classify eachresponse as either being relevant to the intent or irrelevant to theintent. Any responses classified as being irrelevant to the intent maybe discarded by the intent messaging service 502. Responses that areclassified as being relevant to the intent may be presented to thecustomer 506 via the user interface 510. Further, each response may beprovided with options for the customer 506 to invite the other user 504to a conversation with regard to the intent, to thank the other user 504for their response, to ignore the response from the other user 504, toignore future responses from the other user 504 (e.g., block the otheruser 504), and the like.

In an embodiment, the customer 506, via the user interface 510, canconverse with one or more of its contacts while awaiting responses to anintent submitted by the customer 506 to the intent messaging service502. For instance, the intent messaging application may present thecustomer 506 with a user interface 510 that is specific to the intentsubmitted by the customer 506 to the intent messaging service 502.Through this user interface 510, the customer 506 may receive relevantresponses from other users 504 and brands to which the intent isprovided by the intent messaging service 504. Further, through this userinterface 510, the customer 506 may invite any known contacts to aconversation with regard to the intent. For instance, using the intentmessaging application provided by the intent messaging service 502, thecustomer 506 may select one or more contacts to add to the conversationrelated to the intent submitted by the customer 506 to the intentmessaging service 502. These one or more contacts may also be customersof the intent messaging service 502 and may thus also utilize the intentmessaging application to access the intent messaging service 502 andinteract with the customer 506, other users 504, and brands.

In an embodiment, the customer 506 can additionally, or alternatively,select contacts that are unaffiliated with the intent messaging service502. For example, the customer 506 may select one or more contacts froma set of contacts maintained on the computing device 508. The intentmessaging application may utilize contact information of the selectedcontacts to transmit a notification to these selected contactsindicating that these contacts have been invited to engage in aconversation with the customer 506 with regard to the particular intent.The notification may include a Uniform Resource Identifier (URI) orother link to the intent messaging service 502, from which thesecontacts may obtain the intent messaging application needed to accessthe conversation. While awaiting responses from other users 504 andbrands, the customer 506 may converse with any invited contacts via theuser interface 510. These contacts may also utilize an intent messagingapplication provided by the intent messaging service 502 on theirrespective computing devices or through other messaging applicationsthat may interact with the intent messaging service 502 to allow forcommunication between the customer 506 and its contacts.

When a vetted response to an intent is presented to the customer 506 viathe user interface 510, any other participants engaged in theconversation with the customer 506 and associated with the intent mayalso view the response. This may allow these other participants toprovide their own feedback with regard to the vetted response. Forexample, as illustrated in FIG. 5, a participant in the conversationbetween the customer 506 and any selected contacts for a particularintent may be presented with the vetted response provided by anotheruser 504 to the intent messaging service 502 for presentation to thecustomer 506 via the user interface 510. The other participants in theconversation with the customer 506 for the particular intent may providecomments or suggestions to the customer 506 with regard to obtainedresponse from the other user 504.

The customer 506, via the user interface 510, may introduce the otheruser 504 or brand that provided the intent response to the customer 506into the existing conversation related to the intent. For instance, asillustrated in FIG. 5, the response from another user 504 may beprovided with options for the customer 506 to invite the other user 504to the existing conversation between the customer 506 and other contactswith regard to the intent, to thank the other user 504 for theirresponse, to ignore the response from the other user 504, to ignorefuture responses from the other user 504 (e.g., block the other user504), and the like. If the customer 506 invites the other user 504 topartake in the conversation with regard to the intent, the intentmessaging application operating on the computing device 508 may transmita request to the intent messaging service 502 to allow the other user504 to interact with the customer 506 and any other contactsparticipating in the conversation related to the intent. In response tothe request, the intent messaging service 502 may transmit additionalcustomer information to the other user 504. This additional customerinformation may include the customer's name, address, contactinformation, pictures, and the like. Further, the intent messagingservice 502 may allow the other user 504 to interact with the customer506 and any other participant in the conversation regarding the intent.

FIG. 6 shows an illustrative example of an environment 600 in which acustomer 606, via a user interface 610, initiates a communicationschannel with a brand 604 to converse with the brand 604 with regard toan intent in accordance with at least one embodiment. In the environment600, the intent messaging service 602 may provide, via a user interface610 of an intent messaging application operating on a customer'scomputing device 608, a vetted response to an intent submitted by thecustomer 606 to the intent messaging service 602. The vetted response,as illustrated in FIG. 6, may include the name of the brand 604 thatsubmitted the response to the intent. As noted above, the intentmessaging service 602 may evaluate responses from brands 604 and otherusers to determine whether these responses are relevant to a particularintent. For instance, the intent messaging service 602 may utilize aclassification algorithm or other machine learning model to evaluateresponses to an intent to classify each response as either beingrelevant to the intent or irrelevant to the intent. Any responsesclassified as being irrelevant to the intent may be discarded by theintent messaging service 602. Responses that are classified as beingrelevant to the intent may be presented to the customer 606 via the userinterface 610. Further, each response may be provided with options forthe customer 606 to invite the brand 604 to a conversation with regardto the intent, to thank the brand 604 for their response, to ignore theresponse from the brand 604, to ignore future responses from the brand604 (e.g., block the brand 604), and the like.

Through the user interface 610, the customer 606 may invite a brand 604to partake in a conversation with regard to the response provided by thebrand 604 to the intent submitted by the customer 606 to the intentmessaging service 602. For instance, through the user interface 610, thecustomer 606 may select an option to invite a brand 604 to engage in aconversation with the customer 606. If the customer 606 selects thisoption, the intent messaging application on the customer's computingdevice 608 may transmit a request to the intent messaging service 602 toinitiate a communications channel between the customer 606 and the brand604. In response to the request, the intent messaging service 602 maytransmit additional customer information to the brand 604. Thisadditional customer information may include the customer's name,address, contact information, pictures, and the like. Further, theintent messaging service 602 may allow the brand 604 to interact withthe customer 606 via the communications channel established between thecustomer 606 and the brand 604.

Once the communications channel between the customer 606 and the brand604 has been established, the customer 606 may transmit messages to thebrand 604 via the user interface 610. For instance, as illustrated inFIG. 6, the customer 606 may respond to the vetted response of the brand604 to greet the brand 604 and provide additional details with regard toits intent and that may be responsive to the vetted response from thebrand 604. This response from the customer 606 may be transmitted to thebrand 604 over the communications channel established by the intentmessaging service 602. This may result in the response being presentedto the brand 604 via a user interface of an intent messaging applicationutilized by an agent or other user associated with the brand 604. Thebrand 604 may provide additional responses that may be presented to thecustomer 606 via the user interface 610. Thus, through the intentmessaging service 602, the customer 606 and the brand 604 maycommunicate with regard to a particular intent of the customer 606 topotentially identify a resolution to the intent.

FIG. 7 shows an illustrative example of an environment 700 in which acustomer 706, via a user interface 710, provides a response to an intentof another customer in accordance with at least one embodiment. In theenvironment 700, the intent messaging service 702 may select thecustomer 706 to solicit a response to an intent submitted by anotheruser 704 of the intent messaging service 702. As noted above, inresponse to obtaining a request from a user, the intent messagingservice 702 may extract an intent from the request and identify one ormore brands and other users that are likely to have relevant experiencewith regard to the intent and that are likely to provide a relevantresponse to the intent. For instance, the intent messaging service 702,via an intent matching system, may utilize a machine learning model toidentify the one or more brands and other users that are to be providedwith the intent in order to solicit responses to the intent that can bepresented to the user that submitted the request. The machine learningmodel may utilize the intent, as well as brand and user profiles fromrespective databases, as input to generate an output that includesidentifiers of the selected brands and users to which the intent is tobe provided.

As illustrated in FIG. 7, the customer 706 is selected by the intentmessaging service 702 to obtain the intent from another user 704 inorder to solicit a response to the intent from the customer 706. Thecustomer 706 may have been selected by the intent messaging service 702based on one or more factors. For instance, the customer 706 may haveprior experience with regard to the particular intent from the otheruser 704. As an illustrative example, if the other user 704 expresses anintent to identify locations where one can order pet food quickly, theintent messaging service 702 may determine that the customer 706 has hadprior experience in obtaining pet food from different brands orlocations. This prior experience may have been expressed to the intentmessaging service 702 through prior intents submitted by the customer706 to the intent messaging service 702, through responses provided toother users 704 of the intent messaging service 702, or through otherexpressions provided by the customer 706 to the intent messaging service702 (e.g., customer has indicated an interest in pets, customer hasinteracted with brands related to pets and pet care, etc.).

The intent messaging service 702 may update the user interface 710 ofthe intent messaging application installed on the customer's computingdevice 708 to provide the intent of the other user 704. For instance,through the user interface 710, the customer 706 may be presented with anew conversation window that includes the other user's intent. Theintent may be presented without any identifying information of the otheruser 704 that submitted the intent. In some instances, the intent may beprovided with other information that may be of use to the customer 706in providing a response to the intent. For instance, the intent may bepresented, via the user interface 710, with geographic locationinformation of the other user 704, a timeframe for addressing theintent, and the like. This additional information may be used by thecustomer 706 to tailor its response to the intent. For example, if theintent is to identify a location where the other user 704 can obtain petfood quickly, the intent may be provided with a geographic location ofthe other user 704. The customer 706 may use this additional informationto identify a pet food location that is within the vicinity of thespecified geographic location. Thus, this additional information may beuseful to the customer 706 to allow the customer 706 to prepare arelevant response to the intent.

The customer 706, via the user interface 710, may submit a response tothe intent. As illustrated in FIG. 7, the customer 706 has indicated thename of a brand that may be used by the other user 704 to address theintent. Further, the customer 706 may provide other information that maybe of use to the other user 704. For example, the customer 706 mayindicate its own experience with the specified brand, which may beuseful to the other user 704 if it decides to engage the specifiedbrand. Through the user interface 710, the customer 706 may additionallyprovide digital images, voice recordings, video recordings, or othermedia in response to the intent and that may be of use to the other user704.

The intent messaging service 702 may obtain the intent responsesubmitted by the customer 706 via the user interface 710 from thecomputing device 708. In response to obtaining this intent response fromthe customer 706, the intent messaging service 702 may evaluate theintent response to determine whether this intent response is relevant tothe intent submitted by the other user 704. For instance, the intentmessaging service 702 may utilize a classification algorithm or othermachine learning model to classify the intent response submitted by thecustomer 706 as being relevant or irrelevant to the intent. Theclassification algorithm or other machine learning model may be trainedusing supervised learning techniques. For instance, a dataset of inputintents, known relevant responses, known irrelevant responses, andclassifications, can be selected for training of the classificationalgorithm or other machine learning model. In some examples, the inputintents can be obtained from administrators of the intent messagingservice, customers of the intent messaging service, or other sourcesassociated with the intent messaging service. In some implementations,known relevant and irrelevant responses used to train the classificationalgorithm or other machine learning model utilized by the intentprocessing system include responses generated by the entities thatgenerated the sample intents.

If the intent messaging service 702 determines that the responseprovided by the customer 706 to the intent is relevant to the intent,the intent messaging service 702 may provide the response to the otheruser 704. The other user 704 may evaluate the response from the customer706 and determine whether to further engage the customer 706 inconversation through the intent messaging service 702. If so, the intentmessaging service 702 may update the user interface 710 of the customer706 to supply the customer 706 with identifying information of the otheruser 704. Further, the intent messaging service 702 may establish acommunications channel between the customer 706 and the other user 704to allow the customer 706 and other user 704 to converse using theirrespective intent messaging applications.

FIG. 8 shows an illustrative example of a process 800 for obtaining anintent and providing the intent to other users and brands in accordancewith at least one embodiment. The process 800 may be performed by theintent messaging service, which may utilize an intent processing systemto extract an intent from a request generated by the customer and anintent matching system to identify the one or more brands and otherusers to which the intent may be provided in order to solicit a responseto the intent from these one or more brands and other users. Forinstance, in response to obtaining a request to solicit responses from acustomer via an intent messaging application, the intent messagingservice may obtain an intent of the customer and identify brands andother users that may provide responses to the intent. The customer mayuse these responses to determine which brands and users to communicatewith to obtain a resolution to their request.

At step 802, the intent messaging service obtains a request to create anintent. For instance, using an intent messaging application provided bythe intent messaging service and installed on to a customer's computingdevice, the customer may generate a request to solicit one or moreresponses from different brands or other users within a network of usersof the intent messaging service. In the request, the customer mayspecify a name for the request, as well as provide its request that isto be associated with the provided name. The request may include anintent that may be used to identify brands and other users that may beable to provide relevant responses to the customer's request. Examplesof intents can include (for example) topic, sentiment, complexity, andurgency. A topic can include, but is not limited to, a subject, aproduct, a service, a technical issue, a use question, a complaint, arefund request or a purchase request, etc. An intent can be determined,for example, based on a semantic analysis of a message (e.g., byidentifying keywords, sentence structures, repeated words, punctuationcharacters and/or non-article words); user input (e.g., having selectedone or more categories); and/or message-associated statistics (e.g.,typing speed and/or response latency).

In response to the request from the customer, the intent messagingservice, at step 804, persists a new intent for the customer. Forinstance, the intent messaging service may extract, from the request, anintent that may be used to identify brands and users that are likely torespond to the request with relevant information or responses that maybe of use to the customer. Further, the intent messaging service mayassociate the intent with a unique identifier that may be used to trackthe intent and any obtained responses to the intent. This may assist theintent messaging service in identifying responses to the intent from thevarious brands or users to which the intent is to be provided and withproviding relevant responses from these brands or users to the customer.

At step 806, the intent messaging service determines whether additionalinformation is required from the customer that may be used to identifywhich brands and users may be selected to solicit responses to theintent. For instance, the intent messaging service may evaluate therequest and intent to determine whether additional information would beof use in identifying a set of brands and users that can providerelevant responses to the customer's intent. This additional informationmay include a geographic location of the customer, a timeframe forresolution of the request, any customer preferences for particularbrands and/or users, and the like.

If the intent messaging service determines that additional informationis required to supplement the request and intent provided by thecustomer, the intent messaging service, at step 808, can solicitadditional information from the customer associated with the intent. Forinstance, the intent messaging service may utilize natural languageprocessing (NLP) or other artificial intelligence algorithms, via theintent messaging application, to ask the customer various questionsrelated to the customer's intent to solicit this additional information.For instance, if the customer has submitted a request for interiordesign company recommendations, the intent messaging service may ask thecustomer where the customer resides, what the timeframe is forcompletion of a new interior design project, what the budget is for anew interior design project, aspects of the interior design project, andother questions tied to the customer's interior design query. Thecustomer, in response to these additional questions from the intentmessaging service, may provide the additional information that may beused to supplement the intent and identify brands and other users thatare likely to provide relevant responses to the customer's intent.

At step 810, the intent messaging service identifies brands and/or otherusers associated with the intent messaging service that are relevant(e.g., likely to provide relevant responses) to the customer's intent.In an embodiment, the intent messaging service, via an intent matchingsystem, utilizes an intent machine learning modeling engine to identifythe one or more brands and/or other users that are to be provided theintent in order to solicit responses from these one or more brandsand/or other users. The intent machine learning modeling engine mayutilize a brand database and user database maintained by the intentmatching system, as well as the customer's intent, as input to a machinelearning model to identify the brands and/or users that are likely toprovide relevant responses to the intent. The brand database and userdatabase may include profiles of each of the brands and users,respectively, which may be associated with the intent messaging service.Each profile may indicate a user's or brand's experience responding toparticular intents or categories of intents, as well as the user's orbrand's interest in the underlying topic or classification of the intent(e.g., interior design, etc.). Further, each profile may indicatefeedback with regard to a user's or brand's response to previouslyprovided intents. This feedback may specify whether a response providedin response to an intent was relevant, useful, or otherwise appreciatedby the corresponding customer.

The machine learning model utilized by the intent machine learningmodeling engine may be trained using sample intents and sample outputscorresponding to features of brands and other users that may be used toidentify the brands and other users to which an intent is to beprovided. Further, the machine learning model may be trained usingfeedback from the different brands and other users receiving an intent.This feedback may be used to determine whether the machine learningmodel is selecting brands and other users that are capable of respondingto an intent with a relevant response or that are otherwise associatedwith a feature of an intent. For example, if a brand that providesinterior design services obtained an intent that is not related tointerior design, the brand may provide feedback indicating that theprovided intent is not relevant to the brand. This feedback may be usedto further train the machine learning model utilized by the intentmatching system.

At step 812, based on the output generated by the intent machinelearning modeling engine, the intent messaging service recommends otherusers and/or brands from which to solicit responses to the customer'sintent. For instance, the intent messaging service may provide, via theintent messaging application, a listing of the brands and/or other usersidentified by the intent machine learning modeling engine as beinglikely to provide relevant responses to the customer's intent. Using theintent messaging application, the customer may select a subset of otherusers and brands to which its intent is to be provided to solicitresponses from.

Based on the customer's selection, the intent messaging service, at step814, transmits the customer intent to the other users and/or brandsselected by the customer. In an embodiment, if the customer does notmake a selection of brands and/or users to which its intent is to beprovided, the intent messaging service can transmit the customer'sintent to the other users and/or brands identified by the intent machinelearning modeling engine by default. In some instances, the intentmessaging service may select a subset of other users and/or brands bydefault based on a relevancy threshold. For instance, each brand andother user may be scored based on their likelihood to provide a relevantresponse to the customer's intent. This score may be calculated by theintent machine learning modeling engine based on a brand's or user'shistorical interest in the topic of the customer's intent, a brand's oruser's historical experience in responding to similar intents, and thelike.

FIG. 9 shows an illustrative example of a process 900 for evaluatingproposed responses from brands and other users to identify relevantresponses presentable to a customer in response to an intent inaccordance with at least one embodiment. The process 900 may beperformed by the intent messaging service, which may utilize the intentprocessing system to evaluate responses to an intent from differentbrands and users to which the intent was provided in order to determinewhether the responses are relevant to the intent and can be provided tothe customer. In an embodiment, the intent processing system implementsa machine learning model that is used to evaluate the responses from thedifferent brands and users to identify a subset of responses that may beprovided to the customer in response to the customer's intent.

At step 902, the intent processing system obtains one or more proposedresponses to a customer intent from brands and other users to which acustomer intent was provided to solicit a response. As noted above, anintent matching system of the intent matching service may identify oneor more brands and other users that may provide relevant responses to acustomer intent submitted by a customer of the intent messaging service.For instance, the intent matching system may query a brand database anda user database to identify brands and users, respectively, that may beinterested in the intent, have experience responding to similar intents,or may otherwise provide a relevant response to the intent. In anembodiment, the intent matching system utilizes a machine learning modelto identify the brands and other users that are likely to providerelevant responses to the customer intent. This machine learning modelmay be trained using sample intents and sample outputs corresponding tofeatures of brands and other users that may be used to identify thebrands and other users to which an intent is to be provided. Further,the machine learning model may be trained using feedback from thedifferent brands and other users receiving an intent. This feedback maybe used to determine whether the machine learning model is selectingbrands and other users that are capable of responding to an intent witha relevant response or that are otherwise associated with a feature ofan intent. For example, if a brand that provides interior designservices obtained an intent that is not related to interior design, thebrand may provide feedback indicating that the provided intent is notrelevant to the brand. This feedback may be used to further train themachine learning model utilized by the intent matching system.

At step 904, the intent processing system evaluates the proposedresponses provided by one or more brands and other users to determinethe relevance of these proposed responses to the customer intent. Forinstance, the intent processing system may utilize a classificationalgorithm or other machine learning model to evaluate a response andclassify the response as being either relevant to the intent orirrelevant to the intent. The classification algorithm or other machinelearning model may be trained using supervised learning techniques. Forinstance, a dataset of input intents, known relevant responses, knownirrelevant responses, and classifications, can be selected for trainingof the classification algorithm or other machine learning model. In someexamples, the input intents can be obtained from administrators of theintent messaging service, customers of the intent messaging service, orother sources associated with the intent messaging service. In someimplementations, known relevant and irrelevant responses used to trainthe classification algorithm or other machine learning model utilized bythe intent processing system include responses generated by the entitiesthat generated the sample intents.

In some examples, the resulting classifications of the known relevantand irrelevant responses to the sample intents are evaluated todetermine the loss, or error, that can be used to train theclassification algorithm or other machine learning model. For instance,if the classification algorithm or other machine learning modelclassifies a relevant response to a sample intent as being an irrelevantresponse or classifies an irrelevant response to a sample intent asbeing a relevant response, the parameters of the classificationalgorithm or other machine learning model may be adjusted according tothe loss resulting from the misclassification of the responses to thesample intents. These parameters may include weights and biases of theclassification algorithm or other machine learning model utilized by theintent processing system.

In an embodiment, the classification algorithm or other machine learningmodel is also trained to identify characteristics or other features of aresponse, as well as the characteristics or other features of the intentfor which the response was generated. These characteristics or featuresmay be used to classify a response as either being relevant orirrelevant to an intent. For instance, if an intent includes one or morecharacteristics corresponding to an interior design request, theclassification algorithm or other machine learning model may evaluate aresponse to determine whether the response includes one or morecharacteristics that also correspond to interior design. Based on asimilarity among these characteristics, the classification algorithm orother machine learning model may determine a relevancy score that, ifthe relevancy score surpasses a relevancy threshold, may correspond to arelevant response. Thus, using the classification algorithm or othermachine learning model, the intent processing system may evaluate aresponse and classify the response as being either relevant orirrelevant to a particular intent.

At step 906, the intent processing system discards any responses fromthe brands and other users that are not relevant to the customer intent.As noted above, the intent processing system may utilize aclassification algorithm or other machine learning model to classifyeach response obtained from a brand or other user for a particularintent. If the classification algorithm or other machine learning modeldetermines that a response does not satisfy a relevancy threshold, theclassification algorithm or other machine learning model may classifythe response as being irrelevant to the intent. The intent processingsystem may, thus, obtain any responses classified as being irrelevant tothe customer intent and discard these responses. In an embodiment, if abrand or other user is identified as having provided an irrelevantresponse, the intent processing system can provide information regardingthe brand or other user, as well as the customer intent for which thebrand or other user was determined to have provided an irrelevantresponse, to the intent matching system. The intent matching system mayutilize this information to further train its machine learning modelutilized to identify brands and other users that may be selected forsolicitation of responses to particular intents. This may result in thebrand or other user that provided an irrelevant response being lesslikely to be selected by the intent matching system to provide aresponse to an intent similar to the customer intent for which the brandor other user provided an irrelevant response.

At step 908, the intent processing system provides the relevantresponses obtained from the one or more brands and other users to thecustomer to allow for customer interaction with these brands and otherusers via the intent messaging application. For instance, the intentprocessing system may transmit the relevant responses to the intentmessaging application, which may cause the intent messaging applicationto update a GUI to present the available responses to the customer viathe customer's computing device. From this GUI, the customer maydetermine whether to initiate a conversation with a brand or other userthat provided a relevant response. If the customer does not wish toinitiate a conversation with a brand or other user that provided aresponse deemed to be relevant by the intent processing system, thecustomer may instead transmit a notification to the brand or other uservia the intent messaging service thanking the brand or other user forits response. Additionally, or alternatively, the customer may submitfeedback with regard to the obtained response. For instance, thecustomer may indicate whether the provided response was relevant to theintent submitted by the customer. This feedback may be used by theintent processing system to further train the classification algorithmor other machine learning model utilized to identify relevant responsesto given intents. If the customer opts to initiate a conversation with abrand or other user, the intent messaging service may open acommunications channel between the customer and the particular brand orother user. This allows the brand or other user to interact with thecustomer over the communications channel.

FIG. 10 illustrates a computing system architecture 1000 includingvarious components in electrical communication with each other using aconnection 1006, such as a bus, in accordance with some implementations.Example system architecture 1000 includes a processing unit (CPU orprocessor) 1004 and a system connection 1006 that couples various systemcomponents including the system memory 1020, such as ROM 1018 and RAM1016, to the processor 1004. The system architecture 1000 can include acache 1002 of high-speed memory connected directly with, in closeproximity to, or integrated as part of the processor 1004. The systemarchitecture 1000 can copy data from the memory 1020 and/or the storagedevice 1008 to the cache 1002 for quick access by the processor 1004. Inthis way, the cache can provide a performance boost that avoidsprocessor 1004 delays while waiting for data. These and other modulescan control or be configured to control the processor 1004 to performvarious actions.

Other system memory 1020 may be available for use as well. The memory1020 can include multiple different types of memory with differentperformance characteristics. The processor 1004 can include any generalpurpose processor and a hardware or software service, such as service 11010, service 2 1012, and service 3 1014 stored in storage device 1008,configured to control the processor 1004 as well as a special-purposeprocessor where software instructions are incorporated into the actualprocessor design. The processor 1004 may be a completely self-containedcomputing system, containing multiple cores or processors, a bus, memorycontroller, cache, etc. A multi-core processor may be symmetric orasymmetric.

To enable user interaction with the computing system architecture 1000,an input device 1022 can represent any number of input mechanisms, suchas a microphone for speech, a touch-sensitive screen for gesture orgraphical input, keyboard, mouse, motion input, speech and so forth. Anoutput device 1024 can also be one or more of a number of outputmechanisms known to those of skill in the art. In some instances,multimodal systems can enable a user to provide multiple types of inputto communicate with the computing system architecture 1000. Thecommunications interface 1026 can generally govern and manage the userinput and system output. There is no restriction on operating on anyparticular hardware arrangement and therefore the basic features heremay easily be substituted for improved hardware or firmware arrangementsas they are developed.

Storage device 1008 is a non-volatile memory and can be a hard disk orother types of computer readable media which can store data that areaccessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices, digital versatile disks, cartridges,RAMs 1016, ROM 1018, and hybrids thereof.

The storage device 1008 can include services 1010, 1012, 1014 forcontrolling the processor 1004. Other hardware or software modules arecontemplated. The storage device 1008 can be connected to the systemconnection 1006. In one aspect, a hardware module that performs aparticular function can include the software component stored in acomputer-readable medium in connection with the necessary hardwarecomponents, such as the processor 1004, connection 1006, output device1024, and so forth, to carry out the function.

The disclosed methods can be performed using a computing system. Anexample computing system can include a processor (e.g., a centralprocessing unit), memory, non-volatile memory, and an interface device.The memory may store data and/or and one or more code sets, software,scripts, etc. The components of the computer system can be coupledtogether via a bus or through some other known or convenient device. Theprocessor may be configured to carry out all or part of methodsdescribed herein for example by executing code for example stored inmemory. One or more of a user device or computer, a provider server orsystem, or a suspended database update system may include the componentsof the computing system or variations on such a system.

This disclosure contemplates the computer system taking any suitablephysical form, including, but not limited to a Point-of-Sale system(“POS”). As example and not by way of limitation, the computer systemmay be an embedded computer system, a system-on-chip (SOC), asingle-board computer system (SBC) (such as, for example, acomputer-on-module (COM) or system-on-module (SOM)), a desktop computersystem, a laptop or notebook computer system, an interactive kiosk, amainframe, a mesh of computer systems, a mobile telephone, a personaldigital assistant (PDA), a server, or a combination of two or more ofthese. Where appropriate, the computer system may include one or morecomputer systems; be unitary or distributed; span multiple locations;span multiple machines; and/or reside in a cloud, which may include oneor more cloud components in one or more networks. Where appropriate, oneor more computer systems may perform without substantial spatial ortemporal limitation one or more steps of one or more methods describedor illustrated herein. As an example and not by way of limitation, oneor more computer systems may perform in real time or in batch mode oneor more steps of one or more methods described or illustrated herein.One or more computer systems may perform at different times or atdifferent locations one or more steps of one or more methods describedor illustrated herein, where appropriate.

The processor may be, for example, be a conventional microprocessor suchas an Intel Pentium microprocessor or Motorola power PC microprocessor.One of skill in the relevant art will recognize that the terms“machine-readable (storage) medium” or “computer-readable (storage)medium” include any type of device that is accessible by the processor.

The memory can be coupled to the processor by, for example, a bus. Thememory can include, by way of example but not limitation, random accessmemory (RAM), such as dynamic RAM (DRAM) and static RAM (SRAM). Thememory can be local, remote, or distributed.

The bus can also couple the processor to the non-volatile memory anddrive unit. The non-volatile memory is often a magnetic floppy or harddisk, a magnetic-optical disk, an optical disk, a read-only memory(ROM), such as a CD-ROM, EPROM, or EEPROM, a magnetic or optical card,or another form of storage for large amounts of data. Some of this datais often written, by a direct memory access process, into memory duringexecution of software in the computer. The non-volatile storage can belocal, remote, or distributed. The non-volatile memory is optionalbecause systems can be created with all applicable data available inmemory. A typical computer system will usually include at least aprocessor, memory, and a device (e.g., a bus) coupling the memory to theprocessor.

Software can be stored in the non-volatile memory and/or the drive unit.Indeed, for large programs, it may not even be possible to store theentire program in the memory. Nevertheless, it should be understood thatfor software to run, if necessary, it is moved to a computer readablelocation appropriate for processing, and for illustrative purposes, thatlocation is referred to as the memory herein. Even when software ismoved to the memory for execution, the processor can make use ofhardware registers to store values associated with the software, andlocal cache that, ideally, serves to speed up execution. As used herein,a software program is assumed to be stored at any known or convenientlocation (from non-volatile storage to hardware registers), when thesoftware program is referred to as “implemented in a computer-readablemedium.” A processor is considered to be “configured to execute aprogram” when at least one value associated with the program is storedin a register readable by the processor.

The bus can also couple the processor to the network interface device.The interface can include one or more of a modem or network interface.It will be appreciated that a modem or network interface can beconsidered to be part of the computer system. The interface can includean analog modem, Integrated Services Digital network (ISDN0 modem, cablemodem, token ring interface, satellite transmission interface (e.g.,“direct PC”), or other interfaces for coupling a computer system toother computer systems. The interface can include one or more inputand/or output (I/O) devices. The I/O devices can include, by way ofexample but not limitation, a keyboard, a mouse or other pointingdevice, disk drives, printers, a scanner, and other input and/or outputdevices, including a display device. The display device can include, byway of example but not limitation, a cathode ray tube (CRT), liquidcrystal display (LCD), or some other applicable known or convenientdisplay device.

In operation, the computer system can be controlled by operating systemsoftware that includes a file management system, such as a diskoperating system. One example of operating system software withassociated file management system software is the family of operatingsystems known as Windows® from Microsoft Corporation of Redmond, Wash.,and their associated file management systems. Another example ofoperating system software with its associated file management systemsoftware is the Linux™ operating system and its associated filemanagement system. The file management system can be stored in thenon-volatile memory and/or drive unit and can cause the processor toexecute the various acts required by the operating system to input andoutput data and to store data in the memory, including storing files onthe non-volatile memory and/or drive unit.

Some portions of the detailed description may be presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated. It has proven convenient at times, principally for reasonsof common usage, to refer to these signals as bits, values, elements,symbols, characters, terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or “generating” or the like, refer to theaction and processes of a computer system, or similar electroniccomputing device, that manipulates and transforms data represented asphysical (electronic) quantities within registers and memories of thecomputer system into other data similarly represented as physicalquantities within the computer system memories or registers or othersuch information storage, transmission or display devices.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the methods of some examples. The requiredstructure for a variety of these systems will appear from thedescription below. In addition, the techniques are not described withreference to any particular programming language, and various examplesmay thus be implemented using a variety of programming languages.

In various implementations, the system operates as a standalone deviceor may be connected (e.g., networked) to other systems. In a networkeddeployment, the system may operate in the capacity of a server or aclient system in a client-server network environment, or as a peersystem in a peer-to-peer (or distributed) network environment.

The system may be a server computer, a client computer, a personalcomputer (PC), a tablet PC, a laptop computer, a set-top box (STB), apersonal digital assistant (PDA), a cellular telephone, an iPhone, aBlackberry, a processor, a telephone, a web appliance, a network router,switch or bridge, or any system capable of executing a set ofinstructions (sequential or otherwise) that specify actions to be takenby that system.

While the machine-readable medium or machine-readable storage medium isshown, by way of example, to be a single medium, the term“machine-readable medium” and “machine-readable storage medium” shouldbe taken to include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) that store the one or more sets of instructions. The term“machine-readable medium” and “machine-readable storage medium” shallalso be taken to include any medium that is capable of storing,encoding, or carrying a set of instructions for execution by the systemand that cause the system to perform any one or more of themethodologies or modules of disclosed herein.

In general, the routines executed to implement the implementations ofthe disclosure, may be implemented as part of an operating system or aspecific application, component, program, object, module or sequence ofinstructions referred to as “computer programs.” The computer programstypically comprise one or more instructions set at various times invarious memory and storage devices in a computer, and that, when readand executed by one or more processing units or processors in acomputer, cause the computer to perform operations to execute elementsinvolving the various aspects of the disclosure.

Moreover, while examples have been described in the context of fullyfunctioning computers and computer systems, those skilled in the artwill appreciate that the various examples are capable of beingdistributed as a program object in a variety of forms, and that thedisclosure applies equally regardless of the particular type of machineor computer-readable media used to actually effect the distribution.

Further examples of machine-readable storage media, machine-readablemedia, or computer-readable (storage) media include but are not limitedto recordable type media such as volatile and non-volatile memorydevices, floppy and other removable disks, hard disk drives, opticaldisks (e.g., Compact Disk Read-Only Memory (CD ROMS), Digital VersatileDisks, (DVDs), etc.), among others, and transmission type media such asdigital and analog communication links.

In some circumstances, operation of a memory device, such as a change instate from a binary one to a binary zero or vice-versa, for example, maycomprise a transformation, such as a physical transformation. Withparticular types of memory devices, such a physical transformation maycomprise a physical transformation of an article to a different state orthing. For example, but without limitation, for some types of memorydevices, a change in state may involve an accumulation and storage ofcharge or a release of stored charge. Likewise, in other memory devices,a change of state may comprise a physical change or transformation inmagnetic orientation or a physical change or transformation in molecularstructure, such as from crystalline to amorphous or vice versa. Theforegoing is not intended to be an exhaustive list of all examples inwhich a change in state for a binary one to a binary zero or vice-versain a memory device may comprise a transformation, such as a physicaltransformation. Rather, the foregoing is intended as illustrativeexamples.

A storage medium typically may be non-transitory or comprise anon-transitory device. In this context, a non-transitory storage mediummay include a device that is tangible, meaning that the device has aconcrete physical form, although the device may change its physicalstate. Thus, for example, non-transitory refers to a device remainingtangible despite this change in state.

The above description and drawings are illustrative and are not to beconstrued as limiting the subject matter to the precise forms disclosed.Persons skilled in the relevant art can appreciate that manymodifications and variations are possible in light of the abovedisclosure. Numerous specific details are described to provide athorough understanding of the disclosure. However, in certain instances,well-known or conventional details are not described in order to avoidobscuring the description.

As used herein, the terms “connected,” “coupled,” or any variant thereofwhen applying to modules of a system, means any connection or coupling,either direct or indirect, between two or more elements; the coupling ofconnection between the elements can be physical, logical, or anycombination thereof. Additionally, the words “herein,” “above,” “below,”and words of similar import, when used in this application, shall referto this application as a whole and not to any particular portions ofthis application. Where the context permits, words in the above DetailedDescription using the singular or plural number may also include theplural or singular number respectively. The word “or,” in reference to alist of two or more items, covers all of the following interpretationsof the word: any of the items in the list, all of the items in the list,or any combination of the items in the list.

Those of skill in the art will appreciate that the disclosed subjectmatter may be embodied in other forms and manners not shown below. It isunderstood that the use of relational terms, if any, such as first,second, top and bottom, and the like are used solely for distinguishingone entity or action from another, without necessarily requiring orimplying any such actual relationship or order between such entities oractions.

While processes or blocks are presented in a given order, alternativeimplementations may perform routines having steps, or employ systemshaving blocks, in a different order, and some processes or blocks may bedeleted, moved, added, subdivided, substituted, combined, and/ormodified to provide alternative or sub combinations. Each of theseprocesses or blocks may be implemented in a variety of different ways.Also, while processes or blocks are at times shown as being performed inseries, these processes or blocks may instead be performed in parallel,or may be performed at different times. Further any specific numbersnoted herein are only examples: alternative implementations may employdiffering values or ranges.

The teachings of the disclosure provided herein can be applied to othersystems, not necessarily the system described above. The elements andacts of the various examples described above can be combined to providefurther examples.

Any patents and applications and other references noted above, includingany that may be listed in accompanying filing papers, are incorporatedherein by reference. Aspects of the disclosure can be modified, ifnecessary, to employ the systems, functions, and concepts of the variousreferences described above to provide yet further examples of thedisclosure.

These and other changes can be made to the disclosure in light of theabove Detailed Description. While the above description describescertain examples, and describes the best mode contemplated, no matterhow detailed the above appears in text, the teachings can be practicedin many ways. Details of the system may vary considerably in itsimplementation details, while still being encompassed by the subjectmatter disclosed herein. As noted above, particular terminology usedwhen describing certain features or aspects of the disclosure should notbe taken to imply that the terminology is being redefined herein to berestricted to any specific characteristics, features, or aspects of thedisclosure with which that terminology is associated. In general, theterms used in the following claims should not be construed to limit thedisclosure to the specific implementations disclosed in thespecification, unless the above Detailed Description section explicitlydefines such terms. Accordingly, the actual scope of the disclosureencompasses not only the disclosed implementations, but also allequivalent ways of practicing or implementing the disclosure under theclaims.

While certain aspects of the disclosure are presented below in certainclaim forms, the inventors contemplate the various aspects of thedisclosure in any number of claim forms. Any claims intended to betreated under 35 U.S.C. § 112(f) will begin with the words “means for”.Accordingly, the applicant reserves the right to add additional claimsafter filing the application to pursue such additional claim forms forother aspects of the disclosure.

The terms used in this specification generally have their ordinarymeanings in the art, within the context of the disclosure, and in thespecific context where each term is used. Certain terms that are used todescribe the disclosure are discussed above, or elsewhere in thespecification, to provide additional guidance to the practitionerregarding the description of the disclosure. For convenience, certainterms may be highlighted, for example using capitalization, italics,and/or quotation marks. The use of highlighting has no influence on thescope and meaning of a term; the scope and meaning of a term is thesame, in the same context, whether or not it is highlighted. It will beappreciated that same element can be described in more than one way.

Consequently, alternative language and synonyms may be used for any oneor more of the terms discussed herein, nor is any special significanceto be placed upon whether or not a term is elaborated or discussedherein. Synonyms for certain terms are provided. A recital of one ormore synonyms does not exclude the use of other synonyms. The use ofexamples anywhere in this specification including examples of any termsdiscussed herein is illustrative only, and is not intended to furtherlimit the scope and meaning of the disclosure or of any exemplifiedterm. Likewise, the disclosure is not limited to various examples givenin this specification.

Without intent to further limit the scope of the disclosure, examples ofinstruments, apparatus, methods and their related results according tothe examples of the present disclosure are given below. Note that titlesor subtitles may be used in the examples for convenience of a reader,which in no way should limit the scope of the disclosure. Unlessotherwise defined, all technical and scientific terms used herein havethe same meaning as commonly understood by one of ordinary skill in theart to which this disclosure pertains. In the case of conflict, thepresent document, including definitions will control.

Some portions of this description describe examples in terms ofalgorithms and symbolic representations of operations on information.These algorithmic descriptions and representations are commonly used bythose skilled in the data processing arts to convey the substance oftheir work effectively to others skilled in the art. These operations,while described functionally, computationally, or logically, areunderstood to be implemented by computer programs or equivalentelectrical circuits, microcode, or the like. Furthermore, it has alsoproven convenient at times, to refer to these arrangements of operationsas modules, without loss of generality. The described operations andtheir associated modules may be embodied in software, firmware,hardware, or any combinations thereof.

Any of the steps, operations, or processes described herein may beperformed or implemented with one or more hardware or software modules,alone or in combination with other devices. In some examples, a softwaremodule is implemented with a computer program object comprising acomputer-readable medium containing computer program code, which can beexecuted by a computer processor for performing any or all of the steps,operations, or processes described.

Examples may also relate to an apparatus for performing the operationsherein. This apparatus may be specially constructed for the requiredpurposes, and/or it may comprise a general-purpose computing deviceselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a non-transitory,tangible computer readable storage medium, or any type of media suitablefor storing electronic instructions, which may be coupled to a computersystem bus. Furthermore, any computing systems referred to in thespecification may include a single processor or may be architecturesemploying multiple processor designs for increased computing capability.

Examples may also relate to an object that is produced by a computingprocess described herein. Such an object may comprise informationresulting from a computing process, where the information is stored on anon-transitory, tangible computer readable storage medium and mayinclude any implementation of a computer program object or other datacombination described herein.

The language used in the specification has been principally selected forreadability and instructional purposes, and it may not have beenselected to delineate or circumscribe the subject matter. It istherefore intended that the scope of this disclosure be limited not bythis detailed description, but rather by any claims that issue on anapplication based hereon. Accordingly, the disclosure of the examples isintended to be illustrative, but not limiting, of the scope of thesubject matter, which is set forth in the following claims.

Specific details were given in the preceding description to provide athorough understanding of various implementations of systems andcomponents for a contextual connection system. It will be understood byone of ordinary skill in the art, however, that the implementationsdescribed above may be practiced without these specific details. Forexample, circuits, systems, networks, processes, and other componentsmay be shown as components in block diagram form in order not to obscurethe embodiments in unnecessary detail. In other instances, well-knowncircuits, processes, algorithms, structures, and techniques may be shownwithout unnecessary detail in order to avoid obscuring the embodiments.

It is also noted that individual implementations may be described as aprocess which is depicted as a flowchart, a flow diagram, a data flowdiagram, a structure diagram, or a block diagram. Although a flowchartmay describe the operations as a sequential process, many of theoperations can be performed in parallel or concurrently. In addition,the order of the operations may be re-arranged. A process is terminatedwhen its operations are completed, but could have additional steps notincluded in a figure. A process may correspond to a method, a function,a procedure, a subroutine, a subprogram, etc. When a process correspondsto a function, its termination can correspond to a return of thefunction to the calling function or the main function.

Client devices, network devices, and other devices can be computingsystems that include one or more integrated circuits, input devices,output devices, data storage devices, and/or network interfaces, amongother things. The integrated circuits can include, for example, one ormore processors, volatile memory, and/or non-volatile memory, amongother things. The input devices can include, for example, a keyboard, amouse, a key pad, a touch interface, a microphone, a camera, and/orother types of input devices. The output devices can include, forexample, a display screen, a speaker, a haptic feedback system, aprinter, and/or other types of output devices. A data storage device,such as a hard drive or flash memory, can enable the computing device totemporarily or permanently store data. A network interface, such as awireless or wired interface, can enable the computing device tocommunicate with a network. Examples of computing devices includedesktop computers, laptop computers, server computers, hand-heldcomputers, tablets, smart phones, personal digital assistants, digitalhome assistants, as well as machines and apparatuses in which acomputing device has been incorporated.

The term “computer-readable medium” includes, but is not limited to,portable or non-portable storage devices, optical storage devices, andvarious other mediums capable of storing, containing, or carryinginstruction(s) and/or data. A computer-readable medium may include anon-transitory medium in which data can be stored and that does notinclude carrier waves and/or transitory electronic signals propagatingwirelessly or over wired connections. Examples of a non-transitorymedium may include, but are not limited to, a magnetic disk or tape,optical storage media such as compact disk (CD) or digital versatiledisk (DVD), flash memory, memory or memory devices. A computer-readablemedium may have stored thereon code and/or machine-executableinstructions that may represent a procedure, a function, a subprogram, aprogram, a routine, a subroutine, a module, a software package, a class,or any combination of instructions, data structures, or programstatements. A code segment may be coupled to another code segment or ahardware circuit by passing and/or receiving information, data,arguments, parameters, or memory contents. Information, arguments,parameters, data, etc. may be passed, forwarded, or transmitted via anysuitable means including memory sharing, message passing, token passing,network transmission, or the like.

The various examples discussed above may further be implemented byhardware, software, firmware, middleware, microcode, hardwaredescription languages, or any combination thereof. When implemented insoftware, firmware, middleware or microcode, the program code or codesegments to perform the necessary tasks (e.g., a computer-programproduct) may be stored in a computer-readable or machine-readablestorage medium (e.g., a medium for storing program code or codesegments). A processor(s), implemented in an integrated circuit, mayperform the necessary tasks.

Where components are described as being “configured to” perform certainoperations, such configuration can be accomplished, for example, bydesigning electronic circuits or other hardware to perform theoperation, by programming programmable electronic circuits (e.g.,microprocessors, or other suitable electronic circuits) to perform theoperation, or any combination thereof.

The various illustrative logical blocks, modules, circuits, andalgorithm steps described in connection with the implementationsdisclosed herein may be implemented as electronic hardware, computersoftware, firmware, or combinations thereof. To clearly illustrate thisinterchangeability of hardware and software, various illustrativecomponents, blocks, modules, circuits, and steps have been describedabove generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem. Skilled artisans may implement the described functionality invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the present disclosure.

The techniques described herein may also be implemented in electronichardware, computer software, firmware, or any combination thereof. Suchtechniques may be implemented in any of a variety of devices such asgeneral purposes computers, wireless communication device handsets, orintegrated circuit devices having multiple uses including application inwireless communication device handsets and other devices. Any featuresdescribed as modules or components may be implemented together in anintegrated logic device or separately as discrete but interoperablelogic devices. If implemented in software, the techniques may berealized at least in part by a computer-readable data storage mediumcomprising program code including instructions that, when executed,performs one or more of the methods described above. Thecomputer-readable data storage medium may form part of a computerprogram product, which may include packaging materials. Thecomputer-readable medium may comprise memory or data storage media, suchas random access memory (RAM) such as synchronous dynamic random accessmemory (SDRAM), read-only memory (ROM), non-volatile random accessmemory (NVRAM), electrically erasable programmable read-only memory(EEPROM), FLASH memory, magnetic or optical data storage media, and thelike. The techniques additionally, or alternatively, may be realized atleast in part by a computer-readable communication medium that carriesor communicates program code in the form of instructions or datastructures and that can be accessed, read, and/or executed by acomputer, such as propagated signals or waves.

The program code may be executed by a processor, which may include oneor more processors, such as one or more digital signal processors(DSPs), general purpose microprocessors, an application specificintegrated circuits (ASICs), field programmable logic arrays (FPGAs), orother equivalent integrated or discrete logic circuitry. Such aprocessor may be configured to perform any of the techniques describedin this disclosure. A general purpose processor may be a microprocessor;but in the alternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration. Accordingly, the term “processor,” as used herein mayrefer to any of the foregoing structure, any combination of theforegoing structure, or any other structure or apparatus suitable forimplementation of the techniques described herein. In addition, in someaspects, the functionality described herein may be provided withindedicated software modules or hardware modules configured forimplementing a suspended database update system.

The foregoing detailed description of the technology has been presentedfor purposes of illustration and description. It is not intended to beexhaustive or to limit the technology to the precise form disclosed.Many modifications and variations are possible in light of the aboveteaching. The described embodiments were chosen in order to best explainthe principles of the technology, its practical application, and toenable others skilled in the art to utilize the technology in variousembodiments and with various modifications as are suited to theparticular use contemplated. It is intended that the scope of thetechnology be defined by the claim.

What is claimed is:
 1. A computer-implemented method comprising:obtaining an intent, wherein the intent corresponds to a request that isto be addressed, and wherein the intent is associated with a customer;identifying one or more users to which to provide the intent, whereinthe one or more users are identified based on a set of characteristicsof the intent; providing the intent, wherein the intent is provided tothe one or more users to solicit responses to the intent; obtaining aset of responses to the intent; evaluating the set of responses toidentify relevant responses to the intent, wherein irrelevant responsesfrom the set of responses are discarded; and providing the relevantresponses to cause the relevant responses to be presented to thecustomer in response to the intent.
 2. The computer-implemented methodof claim 1, further comprising: receiving a request to initiate acommunications channel with a particular user, wherein the request toinitiate the communications channel is associated with the intent and aresponse of the particular user to the intent; and establishing thecommunications channel to allow the particular user to transmitcommunications responsive to the intent over the communications channel.3. The computer-implemented method of claim 1, wherein the set ofresponses are evaluated using a classification model, and wherein theclassification model is updated using sample intents, known relevantresponses to the sample intents, and known irrelevant responses to thesample intents.
 4. The computer-implemented method of claim 1, whereinthe one or more users are identified using a machine learning model, andwherein the machine learning model is updated using sample intents andsample outputs corresponding to features of the one or more users. 5.The computer-implemented method of claim 1, further comprising:transmitting instructions to an application to cause the application toprohibit creation of new responses to the intent, wherein theinstructions are transmitted in response to obtaining the set ofresponses.
 6. The computer-implemented method of claim 1, wherein theintent is extracted from the request based on a semantic analysis of therequest.
 7. The computer-implemented method of claim 1, wherein the setof characteristics of the intent are obtained in response to a query foradditional information associated with the intent.
 8. Thecomputer-implemented method of claim 1, further comprising: utilizingnatural language processing to solicit information associated with theintent, wherein the information associated with the intent is used toextract the intent from the request.
 9. A system, comprising: one ormore processors; and memory storing thereon instructions that, as aresult of being executed by the one or more processors, cause the systemto: obtain an intent, wherein the intent corresponds to a request thatis to be addressed, and wherein the intent is associated with acustomer; identify one or more users to which to provide the intent,wherein the one or more users are identified based on a set ofcharacteristics of the intent; provide the intent, wherein the intent isprovided to the one or more users to solicit responses to the intent;obtain a set of responses to the intent; evaluate the set of responsesto identify relevant responses to the intent, wherein irrelevantresponses from the set of responses are discarded; and provide therelevant responses to cause the relevant responses to be presented tothe customer in response to the intent.
 10. The system of claim 9,wherein the instructions, as a result of being executed by the one ormore processors, further cause the system to: obtain a request toestablish a communications channel between a first computing system ofthe customer and a second computing system of a particular user, whereinthe request to establish the communications channel is obtained inresponse to a determination that a response from the particular usersatisfies the intent; and establish the communications channel inresponse to the request to establish the communications channel.
 11. Thesystem of claim 9, wherein the relevant responses are identified using aclassification model, and wherein the classification model is generatedusing sample intents, known relevant responses to the sample intents,and known irrelevant responses to the sample intents.
 12. The system ofclaim 9, wherein the one or more users are identified using a machinelearning model, and wherein the machine learning model is generatedusing sample intents and sample outputs corresponding to features of theone or more users.
 13. The system of claim 9, wherein the instructions,as a result of being executed by the one or more processors, furthercause the system to: transmit particular instructions to an applicationto cause the application to prohibit further responses to the intentfrom being generated, and wherein the particular instructions aretransmitted in response to obtaining the set of responses.
 14. Thesystem of claim 9, wherein the instructions, as a result of beingexecuted by the one or more processors, further cause the system to:perform a semantic analysis of the request to extract the intent fromthe request.
 15. The system of claim 9, wherein the set ofcharacteristics of the intent are obtained in response to a query foradditional information associated with the intent.
 16. The system ofclaim 9, wherein the instructions, as a result of being executed by theone or more processors, further cause the system to: utilize naturallanguage processing to solicit information associated with the intent,wherein the information associated with the intent is used to extractthe intent from the request.
 17. A non-transitory, computer-readablestorage medium storing thereon executable instructions that, as a resultof being executed by one or more processors of a computer system, causethe computer system to: obtain an intent, wherein the intent correspondsto a request that is to be addressed, and wherein the intent isassociated with a customer; identify one or more users to which toprovide the intent, wherein the one or more users are identified basedon a set of characteristics of the intent; provide the intent, whereinthe intent is provided to the one or more users to solicit responses tothe intent; obtain a set of responses to the intent; evaluate the set ofresponses to identify relevant responses to the intent, whereinirrelevant responses from the set of responses are discarded; andprovide the relevant responses to cause the relevant responses to bepresented to the customer in response to the intent.
 18. Thenon-transitory, computer-readable medium of claim 17, wherein theexecutable instructions, as a result of being executed by the one ormore processors, further cause the computer system to: obtain a requestto establish a communications channel between a first computing deviceof the customer and a second computing device of a particular user thatprovided a relevant response; and establish the communications channelbetween the first computing device of the customer and the secondcomputing device of the particular user to cause interactions betweenthe customer and the particular user over the communications channelwith regard to the intent.
 19. The non-transitory, computer-readablestorage medium of claim 17, wherein the executable instructions, as aresult of being executed by the one or more processors, further causethe computer system to: use a classification model to evaluate the setof responses, wherein the classification model is updated using sampleintents, known relevant responses to the sample intents, and knownirrelevant responses to the sample intents.
 20. The non-transitory,computer-readable storage medium of claim 17, wherein the one or moreusers are identified using a machine learning model, and wherein themachine learning model is generated using sample intents and sampleoutputs corresponding to features of the one or more users.
 21. Thenon-transitory, computer-readable storage medium of claim 17, whereinthe executable instructions, as a result of being executed by the one ormore processors, further cause the computer system to: transmitparticular instructions to an application to cause the application toprohibit creation of new responses to the intent, wherein the particularinstructions are transmitted in response to obtaining the set ofresponses.
 22. The non-transitory, computer-readable storage medium ofclaim 17, wherein the executable instructions, as a result of beingexecuted by the one or more processors, further cause the computersystem to: perform a semantic analysis of the request to obtain theintent.
 23. The non-transitory, computer-readable storage medium ofclaim 17, wherein the set of characteristics of the intent are obtainedin response to a query for additional information associated with theintent.
 24. The non-transitory, computer-readable storage medium ofclaim 17, wherein the executable instructions, as a result of beingexecuted by the one or more processors, further cause the computersystem to: utilize natural language processing to solicit informationassociated with the intent, wherein the information associated with theintent is used to extract the intent from the request.